Small digital cameras continue to increase in accuracy, detail, and speed and are often accompanied by or accessed by substantial computing resources. These small cameras find use in a wide range of different products such as watches, eyewear, helmets, computing tablets, media players, cellular telephones, computers, and work stations of all kinds. In addition to consumer gear, such cameras are being used in professional and industrial field as well for machine vision and many other applications.
For imaging, machine learning systems, and machine vision functionality shadows can interfere with a system's ability to detect and recognize an object. As an example, for hand gesture recognition, different fingers and finger positions may not be visible due to shadows. This may cause a failure of the detection and recognition system or an erroneous interpretation of a gesture.
In some cases shadows are addressed by trying to estimate the shadow and then compensate for it. As an example, the position and intensity of the light source is projected based on an image. Based on the projection, an estimated shadow is removed using information about object geometry. Another approach analyzes an image for luma gradients with constant chroma values. As in the other case, the object texture and geometry are used to estimate and remove shadow. With shadows accurately removed, the gesture recognition system or any other machine vision system operates more reliably and accurately.